_:n5b093bc754af4c7a84698a5bf60ea9b3xb17
<http://schema.org/description>
"Modeling interactions between features improves the performance of machine\nlearning solutions in many domains (e.g. recommender systems or sentiment\nanalysis). In this paper, we introduce Exponential Machines (ExM), a predictor\nthat models all interactions of every order. The key idea is to represent an\nexponentially large tensor of parameters in a factorized format called Tensor\nTrain (TT). The Tensor Train format regularizes the model and lets you control\nthe number of underlying parameters. To train the model, we develop a\nstochastic Riemannian optimization procedure, which allows us to fit tensors\nwith 2^160 entries. We show that the model achieves state-of-the-art\nperformance on synthetic data with high-order interactions and that it works on\npar with high-order factorization machines on a recommender system dataset\nMovieLens 100K."
<https://deepai.org/organization/hse-university>